Self-Anchoring Study 2 Analyses

library(groundhog)

## Loaded 'groundhog' (version:2.0.1) using R-4.2.1

## Tips and troubleshooting: https://groundhogR.com

## groundhog says:

## 
## 
## 
##           OUTDATED GROUNDHOG
##             You are using version  '2.0.1
##             The current version is '2.1.0'
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##             You can read about the changes here: https://groundhogr.com/changelog
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## Update by running: 
## install.packages('groundhog')

pkgs <-  c("lmerTest", "ggeffects","r2glmm", "tidyverse","here", "sjPlot", "ggpubr", "wesanderson", "effectsize","broom.mixed","corrr","report", "ez", "ggdist")
groundhog.day <- '2022-07-25'
groundhog.library(pkgs, groundhog.day)

## Loading required package: lme4

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here::i_am("Study 2/Analysis/SAs2_Analysis.Rmd")

## here() starts at /Users/jacobelder/Documents/GitHub/SelfAnchoring

#plotDir <- "/Volumes/Research Project/Trait_TestRetest/WeekTRT/plots/"

devtools::source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/corToOne.R")

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devtools::source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/plotCommAxes.R")

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devtools::source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/named.effects.ref.R")

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# fullTest <- read.csv("../Cleaning/output/fullTest.csv")
# fullTrain <- read.csv("../Cleaning/output/fullTrain.csv")
# traitsFreqs <- read.csv("../Cleaning/output/traitFreqOverUnder.csv")

fullTest <- arrow::read_parquet("../Cleaning/output/fullTest.parquet")
fullTrain <- arrow::read_parquet("../Cleaning/output/fullTrain.parquet")
traitsFreqs <- arrow::read_parquet("../Cleaning/output/traitFreqOverUnder.parquet")

uSubs <- unique(fullTest$subID)

indDiffs <- fullTest[!duplicated(fullTest$subID),]

allPosCents <- read.csv("/Volumes/GoogleDrive/My Drive/Volumes/Research Project/Trait Network_Behaviral/generating network/output/allPosCents.csv")

fullTest$ingChoiceN <- as.factor(fullTest$ingChoiceN)
fullTest$novel <- as.factor(fullTest$novel)
fullTest$selfResp.Z <- scale(fullTest$selfResp)
fullTest$SE.Z <- scale(fullTest$SE)
fullTest$iSE.Z <- scale(fullTest$iSE)
fullTest$oSE.Z <- scale(fullTest$oSE)
fullTest$predicted.Z <- scale(fullTest$predicted)
fullTest$slope.Z <- scale(fullTest$slope)
fullTest$entropy.Z <- scale(fullTest$entropy)
fullTest$WSR.Z <- scale(fullTest$WSR)
fullTest$neighAveOutSE.Z <- scale(fullTest$neighAveOutSE)
fullTest$neighAveAllSE.Z <- scale(fullTest$neighAveAllSE)
fullTest$neighAveInSE.Z <- scale(fullTest$neighAveInSE)
fullTest$evalLOO.Z <- scale(fullTest$evalLOO)
fullTest$propCorrInLOO.Z <- scale(fullTest$propCorrInLOO)
fullTest$propCorrOutLOO.Z <- scale(fullTest$propCorrOutLOO)
#fullTest$propCorr.Z <- scale(fullTest$propCorr)
fullTest$desirability.Z <- scale(fullTest$desirability)
fullTest$er.Z <- scale(fullTest$er)
fullTest$inDegree.Z <- scale(fullTest$inDegree)
fullTest$outDegree.Z <- scale(fullTest$outDegree)
fullTest$Ent.Z <- scale(fullTest$Ent)

fullTest$outgroup <- as.factor(fullTest$outgroup)
fullTest$outgroup <- relevel(fullTest$outgroup,"Not UCR")

fullTest$OtherTherm <- rowMeans(fullTest[c("Therm_2","Therm_4")])
fullTest$InOtherTherm <- fullTest$Therm_1 - fullTest$OtherTherm
fullTest$OtherStatus <- rowMeans(fullTest[c("UCLA_Status","CSULA_Status")])
fullTest$InOtherStatus <- fullTest$UCR_Status - fullTest$OtherStatus

fullTest$InOutStatus <- ifelse(fullTest$outgroup == "UCLA", fullTest$InUCLAStatus, 
       ifelse(fullTest$outgroup == "CSU LA", fullTest$InCSULAStatus,
              ifelse(fullTest$outgroup == "Not UCR", fullTest$InOtherStatus, NA)))
fullTest$InOutTherm <- ifelse(fullTest$outgroup == "UCLA", fullTest$InUCLATherm, 
       ifelse(fullTest$outgroup == "CSU LA", fullTest$InCSULATherm,
              ifelse(fullTest$outgroup == "Not UCR", fullTest$InOtherTherm, NA)))

fullTest$novel <- as.factor(fullTest$novel)
levels(fullTest$novel) <- list("Trained"  = "0", "Held Out" = "1")

fullTest$outgroup <- as.factor(fullTest$outgroup)
fullTest$outgroup <- relevel(fullTest$outgroup,"Not UCR")

PCA<- prcomp(na.omit(fullTest[c("predicted","neighAveOutSE")]),
                center = TRUE,
                scale. = TRUE)
fullTest$PCA[!is.na(fullTest$predicted) & !is.na(fullTest$neighAveOutSE)] <- PCA$x[,1]

propMatrix <- matrix(nrow=148,ncol=7)
for(i in 1:148){
    traitDf <- subset(fullTest, Idx==i)
    test <- t.test(as.numeric(traitDf$ingChoiceN)-1, mu=.50)
    propMatrix[i, ] <- c(i, test$statistic, test$p.value, test$conf.int, test$estimate, test$parameter)
}
colnames(propMatrix) <- c("Idx", "stat", "p", "LCI", "UCI", "est", "param")
propMatrix <- as.data.frame(propMatrix)
propMatrix$trait <- traitsFreqs$trait[1:148]
propMatrix <- propMatrix[order(propMatrix$p),]

propMatrix

Are there differences in the perceptions of status between universities?

UCR students perceive UCLA as being significantly higher status and CSU LA students as being significantly lower status.

indDiffs$Status <- NULL
statusDf <- pivot_longer(indDiffs, cols=ends_with("_Status"), names_to="University", values_to="Status") %>% select(subID, University, Status) %>% drop_na()
statusDf$University <- gsub("_Status","", statusDf$University)

statusDf$University <- as.factor(statusDf$University)
statusDf$subID <- as.factor(statusDf$subID)
statusDf$University <- relevel(statusDf$University, ref = "UCR")

m <- lmer( scale(Status) ~ University +  ( 1  | subID), data = statusDf)
summary(m)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(Status) ~ University + (1 | subID)
##    Data: statusDf
## 
## REML criterion at convergence: 1169.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.1951 -0.5157  0.0584  0.5731  2.8926 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.08838  0.2973  
##  Residual             0.33374  0.5777  
## Number of obs: 599, groups:  subID, 200
## 
## Fixed effects:
##                  Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)      -0.14003    0.04594 548.12194  -3.048  0.00242 ** 
## UniversityCSULA  -0.71545    0.05786 397.80645 -12.366  < 2e-16 ***
## UniversityUCLA    1.13170    0.05777 397.29760  19.590  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) UCSULA
## UnvrstCSULA -0.628       
## UnvrstyUCLA -0.629  0.499

ggpredict(m, c("University")) %>% plot()

m<-ezANOVA(statusDf, dv = Status, wid = subID, between = as.factor(University) )

## Warning: The column supplied as the wid variable contains non-unique values
## across levels of the supplied between-Ss variables. Automatically fixing this by
## generating unique wid labels.

## Warning: Data is unbalanced (unequal N per group). Make sure you specified a
## well-considered value for the type argument to ezANOVA().

## Coefficient covariances computed by hccm()

m

## $ANOVA
##       Effect DFn DFd        F             p p<.05       ges
## 1 University   2 596 410.2656 9.077901e-113     * 0.5792539
## 
## $`Levene's Test for Homogeneity of Variance`
##   DFn DFd      SSn      SSd        F          p p<.05
## 1   2 596 5.413247 569.5049 2.832544 0.05965564

ggplot(statusDf, aes(University, Status)) + 
  ggdist::stat_halfeye(adjust = .5, width = .7, .width = 0, justification = -.2, point_colour = NA) + 
  geom_boxplot(width = .2, outlier.shape = NA) + 
  geom_jitter(width = .05, alpha = .3) + labs(y="Perceived Status",x="University") + jtools::theme_apa()

Do people feel differ in how warmly they feel towards each university?

UCR students feel the most warmth towards UCR, significantly less towards UCLA, and the least towards CSU LA.

warmthDf <- pivot_longer(indDiffs, cols=starts_with("Therm_"), names_to="University", values_to="Warmth") %>% select(subID, University, Warmth) %>% drop_na()
warmthDf$University <- gsub("Therm_1","UCR", warmthDf$University)
warmthDf$University <- gsub("Therm_2","UCLA", warmthDf$University)
warmthDf$University <- gsub("Therm_4","CSU LA", warmthDf$University)

warmthDf$University <- as.factor(warmthDf$University)
warmthDf$subID <- as.factor(warmthDf$subID)
warmthDf$University <- relevel(warmthDf$University, ref = "UCR")

m <- lmer( scale(Warmth) ~ University +  ( 1  | subID), data = warmthDf)
summary(m)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(Warmth) ~ University + (1 | subID)
##    Data: warmthDf
## 
## REML criterion at convergence: 1331.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.1404 -0.5117  0.1239  0.6562  2.3007 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.08294  0.288   
##  Residual             0.64005  0.800   
## Number of obs: 529, groups:  subID, 197
## 
## Fixed effects:
##                   Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)        0.51774    0.06120 514.28694   8.460 2.78e-16 ***
## UniversityCSU LA  -1.30600    0.08721 364.74221 -14.976  < 2e-16 ***
## UniversityUCLA    -0.41053    0.08272 344.19662  -4.963 1.09e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) UCSULA
## UnvrstCSULA -0.623       
## UnvrstyUCLA -0.656  0.463

ggpredict(m, c("University")) %>% plot()

m<-ezANOVA(warmthDf, dv = Warmth, wid = subID, between = as.factor(University) )

## Warning: The column supplied as the wid variable contains non-unique values
## across levels of the supplied between-Ss variables. Automatically fixing this by
## generating unique wid labels.

## Warning: Data is unbalanced (unequal N per group). Make sure you specified a
## well-considered value for the type argument to ezANOVA().

## Coefficient covariances computed by hccm()

m

## $ANOVA
##       Effect DFn DFd        F            p p<.05      ges
## 1 University   2 526 102.2002 3.182127e-38     * 0.279847
## 
## $`Levene's Test for Homogeneity of Variance`
##   DFn DFd      SSn      SSd        F            p p<.05
## 1   2 526 3624.271 90108.74 10.57815 3.132948e-05     *

indDiffs$groupHomoph.Z <- scale(indDiffs$groupHomoph)
ggplot(warmthDf, aes(University, Warmth)) + 
  ggdist::stat_halfeye(adjust = .5, width = .7, .width = 0, justification = -.2, point_colour = NA) + 
  geom_boxplot(width = .2, outlier.shape = NA) + 
  geom_jitter(width = .05, alpha = .3) + labs(y="Perceived Warmth",x="University") + jtools::theme_apa()

Correlates with Assortativity in Group Predictioons

Dialectical self and need to belong positively associated with group assortativity. Social identification, self-esteem, identity-centrality, public collectivr eself-est

indDiffs %>% select(groupHomoph, seHomoph, SStatus:IdImp) %>% corToOne(., "groupHomoph")

## [1] "All required packages attached"

## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(referenceVar)` instead of `referenceVar` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.

indDiffs %>% select(groupHomoph, seHomoph, SStatus:IdImp) %>% plotCorToOne(., "groupHomoph")

## [1] "All required packages attached"

## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

Differences in assortativity of group predictions

library(ez)
m<-ezANOVA(indDiffs[!is.na(indDiffs$groupHomoph),], dv=groupHomoph, wid=subID, between=outgroup)

## Warning: Converting "subID" to factor for ANOVA.

## Warning: Converting "outgroup" to factor for ANOVA.

## Warning: Data is unbalanced (unequal N per group). Make sure you specified a
## well-considered value for the type argument to ezANOVA().

## Coefficient covariances computed by hccm()

hsd <- TukeyHSD(aov(groupHomoph ~ outgroup, data=indDiffs[!is.na(indDiffs$groupHomoph),]))
print(m)

## $ANOVA
##     Effect DFn DFd        F            p p<.05       ges
## 1 outgroup   2 194 18.22331 5.590415e-08     * 0.1581565
## 
## $`Levene's Test for Homogeneity of Variance`
##   DFn DFd        SSn       SSd        F          p p<.05
## 1   2 194 0.01989084 0.4295431 4.491777 0.01238983     *

print(hsd)

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = groupHomoph ~ outgroup, data = indDiffs[!is.na(indDiffs$groupHomoph), ])
## 
## $outgroup
##                        diff         lwr        upr     p adj
## Not UCR-CSU LA -0.007553224 -0.03543940 0.02033295 0.7983771
## UCLA-CSU LA     0.057477795  0.02969826 0.08525733 0.0000064
## UCLA-Not UCR    0.065031019  0.03714485 0.09291719 0.0000003

ezPlot(indDiffs[!is.na(indDiffs$groupHomoph),], groupHomoph, wid=subID, between=outgroup, x=.(outgroup))

## Warning: Converting "subID" to factor for ANOVA.

## Warning: Converting "outgroup" to factor for ANOVA.

## Warning: Data is unbalanced (unequal N per group). Make sure you specified a
## well-considered value for the type argument to ezANOVA().

## Coefficient covariances computed by hccm()

## Warning in ezStats(data = data, dv = dv, wid = wid, within = within, within_full
## = within_full, : Unbalanced groups. Mean N will be used in computation of FLSD

indDiffs$groupHomoph.Z <- scale(indDiffs$groupHomoph)
m <- lm(groupHomoph.Z ~ outgroup, data=indDiffs)
summary(m)

## 
## Call:
## lm(formula = groupHomoph.Z ~ outgroup, data = indDiffs)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.8578 -0.6006 -0.1222  0.4185  2.9193 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      -0.2288     0.1135  -2.016   0.0452 *  
## outgroupNot UCR  -0.1031     0.1612  -0.640   0.5231    
## outgroupUCLA      0.7845     0.1605   4.887 2.14e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9222 on 194 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.1582, Adjusted R-squared:  0.1495 
## F-statistic: 18.22 on 2 and 194 DF,  p-value: 5.59e-08

ggpredict(m, c("outgroup")) %>% plot(show.title=F, add.data=T)

ggplot(indDiffs, aes(outgroup, groupHomoph.Z)) + 
  ggdist::stat_halfeye(adjust = .5, width = .7, .width = 0, justification = -.2, point_colour = NA) + 
  geom_boxplot(width = .2, outlier.shape = NA) + 
  geom_jitter(width = .05, alpha = .3)

## Warning: Removed 3 rows containing missing values (stat_slabinterval).

## Warning: Removed 3 rows containing non-finite values (stat_boxplot).

## Warning: Removed 3 rows containing missing values (geom_point).

(Attempt to) replicate prior work: Ingroup favoritism exhibited by ascribing more positive traits to ingroup

Desirability predicts ingroup predictions.

m <- glmer( ingChoiceN ~ desirability.Z +  ( desirability.Z  | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: ingChoiceN ~ desirability.Z + (desirability.Z | subID) + (1 |  
##     trait)
##    Data: fullTest
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
## 
##      AIC      BIC   logLik deviance df.resid 
##  35470.1  35519.8 -17729.1  35458.1    29325 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.3124 -0.9143  0.4457  0.7630  2.8230 
## 
## Random effects:
##  Groups Name           Variance Std.Dev. Corr
##  subID  (Intercept)    0.76943  0.8772       
##         desirability.Z 0.05154  0.2270   0.58
##  trait  (Intercept)    0.14360  0.3789       
## Number of obs: 29331, groups:  subID, 200; trait, 148
## 
## Fixed effects:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)     0.54560    0.07082   7.704 1.32e-14 ***
## desirability.Z  0.26431    0.03751   7.047 1.83e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## desirblty.Z 0.225

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.726 (1.502 – 1.983)	0.122	7.704	<0.001
desirability Z	1.303 (1.210 – 1.402)	0.049	7.047	<0.001
Random Effects
σ2	3.29
τ00 subID	0.77
τ00 trait	0.14
τ11 subID.desirability.Z	0.05
ρ01 subID	0.58
ICC	0.23
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.016 / 0.239

Desirability predicts ingroup predictions most strongly for not CSU LA, less so for UCLA, and least for CSU LA.

m <- glmer( ingChoiceN ~ desirability.Z * outgroup +  ( desirability.Z  | subID) + ( outgroup | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: ingChoiceN ~ desirability.Z * outgroup + (desirability.Z | subID) +  
##     (outgroup | trait)
##    Data: fullTest
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
## 
##      AIC      BIC   logLik deviance df.resid 
##  34327.2  34451.5 -17148.6  34297.2    29316 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.3269 -0.8549  0.4170  0.7136  4.5129 
## 
## Random effects:
##  Groups Name           Variance Std.Dev. Corr       
##  subID  (Intercept)    0.62040  0.7877              
##         desirability.Z 0.05315  0.2305   0.65       
##  trait  (Intercept)    0.32728  0.5721              
##         outgroupCSU LA 0.43552  0.6599   -0.89      
##         outgroupUCLA   0.64980  0.8061   -0.12 -0.18
## Number of obs: 29331, groups:  subID, 200; trait, 148
## 
## Fixed effects:
##                               Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                    0.97510    0.11056   8.820  < 2e-16 ***
## desirability.Z                 0.37058    0.06023   6.153 7.59e-10 ***
## outgroupCSU LA                -0.23383    0.15084  -1.550  0.12109    
## outgroupUCLA                  -1.04611    0.15608  -6.703 2.05e-11 ***
## desirability.Z:outgroupCSU LA -0.20015    0.07512  -2.664  0.00771 ** 
## desirability.Z:outgroupUCLA   -0.07658    0.08493  -0.902  0.36722    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) dsrb.Z oCSULA otUCLA d.Z:CL
## desirblty.Z  0.286                            
## outgrpCSULA -0.736 -0.209                     
## outgropUCLA -0.602 -0.202  0.397              
## dsr.Z:CSULA -0.228 -0.813  0.326  0.162       
## dsrb.Z:UCLA -0.203 -0.352  0.148  0.278  0.117

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	2.651 (2.135 – 3.293)	0.293	8.820	<0.001
desirability Z	1.449 (1.287 – 1.630)	0.087	6.153	<0.001
outgroup [CSU LA]	0.791 (0.589 – 1.064)	0.119	-1.550	0.121
outgroup [UCLA]	0.351 (0.259 – 0.477)	0.055	-6.703	<0.001
desirability Z * outgroup [CSU LA]	0.819 (0.707 – 0.948)	0.061	-2.664	0.008
desirability Z * outgroup [UCLA]	0.926 (0.784 – 1.094)	0.079	-0.902	0.367
Random Effects
σ2	3.29
τ00 subID	0.62
τ00 trait	0.33
τ11 subID.desirability.Z	0.05
τ11 trait.outgroupCSU LA	0.44
τ11 trait.outgroupUCLA	0.65
ρ01 subID	0.65
ρ01 trait.outgroupCSU LA	-0.89
ρ01 trait.outgroupUCLA	-0.12
ICC	0.25
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.061 / 0.296

ggpredict(m, c("desirability.Z","outgroup")) %>% plot(show.title=F) + xlab("Desirability") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()

## Data were 'prettified'. Consider using `terms="desirability.Z [all]"` to get smooth plots.

Replication of prior self-anchoring findings: Self-evaluations predicting ingroup evaluations

No covariates

Self-descriptiveness predict ingroup predictions

m <- glmer( ingChoiceN ~ selfResp.Z  +  ( selfResp.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.690 (1.480 – 1.931)	0.115	7.727	<0.001
selfResp Z	1.325 (1.236 – 1.419)	0.047	7.977	<0.001
Random Effects
σ2	3.29
τ00 subID	0.67
τ00 trait	0.14
τ11 subID.selfResp.Z	0.15
ρ01 subID	0.23
ICC	0.22
N subID	200
N trait	148
Observations	17399
Marginal R2 / Conditional R2	0.018 / 0.238

With covariates

m <- glmer( ingChoiceN ~ selfResp.Z  + desirability.Z +  ( selfResp.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.693 (1.488 – 1.925)	0.111	8.001	<0.001
selfResp Z	1.316 (1.229 – 1.410)	0.046	7.830	<0.001
desirability Z	1.233 (1.161 – 1.308)	0.038	6.868	<0.001
Random Effects
σ2	3.29
τ00 subID	0.67
τ00 trait	0.09
τ11 subID.selfResp.Z	0.15
ρ01 subID	0.23
ICC	0.22
N subID	200
N trait	148
Observations	17399
Marginal R2 / Conditional R2	0.031 / 0.241

With desirability covariates and differences with outgroup

Self-evaluations are more predictive of ingroup choices for negation and least predictive for CSU LA.

m <- glmer( ingChoiceN ~ selfResp.Z * outgroup + desirability.Z +  ( selfResp.Z | subID) + ( outgroup | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: ingChoiceN ~ selfResp.Z * outgroup + desirability.Z + (selfResp.Z |  
##     subID) + (outgroup | trait)
##    Data: fullTest
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
## 
##      AIC      BIC   logLik deviance df.resid 
##  20515.1  20639.3 -10241.6  20483.1    17383 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.7795 -0.8366  0.4165  0.7034  4.4633 
## 
## Random effects:
##  Groups Name           Variance Std.Dev. Corr       
##  subID  (Intercept)    0.5237   0.7237              
##         selfResp.Z     0.1320   0.3633   0.22       
##  trait  (Intercept)    0.2724   0.5220              
##         outgroupCSU LA 0.4595   0.6778   -0.93      
##         outgroupUCLA   0.6077   0.7795   -0.12 -0.19
## Number of obs: 17399, groups:  subID, 200; trait, 148
## 
## Fixed effects:
##                           Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                0.92908    0.10519   8.832  < 2e-16 ***
## selfResp.Z                 0.39008    0.05952   6.554 5.62e-11 ***
## outgroupCSU LA            -0.19067    0.14535  -1.312   0.1896    
## outgroupUCLA              -1.02031    0.14940  -6.829 8.53e-12 ***
## desirability.Z             0.17394    0.02367   7.348 2.02e-13 ***
## selfResp.Z:outgroupCSU LA -0.15741    0.08208  -1.918   0.0551 .  
## selfResp.Z:outgroupUCLA   -0.18701    0.08413  -2.223   0.0262 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) slfR.Z oCSULA otUCLA dsrb.Z sR.Z:L
## selfResp.Z   0.148                                   
## outgrpCSULA -0.748 -0.107                            
## outgropUCLA -0.607 -0.104  0.393                     
## desirblty.Z  0.004 -0.018  0.010 -0.003              
## slR.Z:CSULA -0.107 -0.720  0.150  0.075 -0.040       
## slfR.Z:UCLA -0.105 -0.702  0.076  0.151 -0.004  0.502

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	2.532 (2.060 – 3.112)	0.266	8.832	<0.001
selfResp Z	1.477 (1.314 – 1.660)	0.088	6.554	<0.001
outgroup [CSU LA]	0.826 (0.622 – 1.099)	0.120	-1.312	0.190
outgroup [UCLA]	0.360 (0.269 – 0.483)	0.054	-6.829	<0.001
desirability Z	1.190 (1.136 – 1.246)	0.028	7.348	<0.001
selfResp Z * outgroup [CSU LA]	0.854 (0.727 – 1.003)	0.070	-1.918	0.055
selfResp Z * outgroup [UCLA]	0.829 (0.703 – 0.978)	0.070	-2.223	0.026
Random Effects
σ2	3.29
τ00 subID	0.52
τ00 trait	0.27
τ11 subID.selfResp.Z	0.13
τ11 trait.outgroupCSU LA	0.46
τ11 trait.outgroupUCLA	0.61
ρ01 subID	0.22
ρ01 trait.outgroupCSU LA	-0.93
ρ01 trait.outgroupUCLA	-0.12
ICC	0.24
N subID	200
N trait	148
Observations	17399
Marginal R2 / Conditional R2	0.072 / 0.294

ggpredict(m, c("selfResp.Z", "outgroup")) %>% plot(show.title=F) + xlab("Self-Descriptiveness") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()

Is it merely just average self-descriptiveness of the trait?

Even while controlling for the average of all other participants’ evaluations on each trait, participants’ evaluations are still predictive of ingroup predictions.

m <- glmer( ingChoiceN ~ selfResp.Z + evalLOO.Z + desirability.Z +  ( evalLOO.Z + selfResp.Z + desirability.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: ingChoiceN ~ selfResp.Z + evalLOO.Z + desirability.Z + (evalLOO.Z +  
##     selfResp.Z + desirability.Z | subID) + (1 | trait)
##    Data: fullTest
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
## 
##      AIC      BIC   logLik deviance df.resid 
##  21030.4  21146.9 -10500.2  21000.4    17384 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.4521 -0.8920  0.4264  0.7479  3.1218 
## 
## Random effects:
##  Groups Name           Variance Std.Dev. Corr             
##  subID  (Intercept)    0.70911  0.8421                    
##         evalLOO.Z      0.02170  0.1473   -0.22            
##         selfResp.Z     0.13816  0.3717    0.24 -0.02      
##         desirability.Z 0.04506  0.2123    0.60  0.03  0.23
##  trait  (Intercept)    0.07885  0.2808                    
## Number of obs: 17399, groups:  subID, 200; trait, 148
## 
## Fixed effects:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)     0.54011    0.06683   8.082 6.37e-16 ***
## selfResp.Z      0.25573    0.03466   7.378 1.61e-13 ***
## evalLOO.Z       0.15309    0.03428   4.465 7.99e-06 ***
## desirability.Z  0.17277    0.03518   4.911 9.08e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) slfR.Z eLOO.Z
## selfResp.Z   0.165              
## evalLOO.Z   -0.053 -0.114       
## desirblty.Z  0.246  0.071 -0.333

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.716 (1.506 – 1.956)	0.115	8.082	<0.001
selfResp Z	1.291 (1.207 – 1.382)	0.045	7.378	<0.001
evalLOO Z	1.165 (1.090 – 1.246)	0.040	4.465	<0.001
desirability Z	1.189 (1.109 – 1.273)	0.042	4.911	<0.001
Random Effects
σ2	3.29
τ00 subID	0.71
τ00 trait	0.08
τ11 subID.evalLOO.Z	0.02
τ11 subID.selfResp.Z	0.14
τ11 subID.desirability.Z	0.05
ρ01 subID.evalLOO.Z	-0.22
ρ01 subID.selfResp.Z	0.24
ρ01 subID.desirability.Z	0.60
ICC	0.23
N subID	200
N trait	148
Observations	17399
Marginal R2 / Conditional R2	0.040 / 0.264

Does similarity-weighted self-evaluation average predict ingroup choices?

m <- glmer( ingChoiceN ~ WSR.Z + ( WSR.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.636 (1.342 – 1.994)	0.165	4.878	<0.001
WSR Z	1.871 (1.433 – 2.443)	0.255	4.607	<0.001
Random Effects
σ2	3.29
τ00 subID	1.29
τ00 trait	0.17
τ11 subID.WSR.Z	2.59
ρ01 subID	0.06
ICC	0.55
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.051 / 0.575

Covariates

m <- glmer( ingChoiceN ~ WSR.Z + desirability.Z + ( WSR.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.639 (1.351 – 1.990)	0.162	4.999	<0.001
WSR Z	1.846 (1.416 – 2.407)	0.250	4.529	<0.001
desirability Z	1.249 (1.174 – 1.329)	0.039	7.059	<0.001
Random Effects
σ2	3.29
τ00 subID	1.28
τ00 trait	0.12
τ11 subID.WSR.Z	2.58
ρ01 subID	0.07
ICC	0.55
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.057 / 0.573

Covariates and Condition Differences

Similarity-wieghted self-evaluations predict later ingroup endorsements most for those with UCLA comparison and least for those with CSU LA comparison.

m <- glmer( ingChoiceN ~ WSR.Z * outgroup + desirability.Z + ( WSR.Z + desirability.Z | subID) + ( outgroup | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	2.154 (1.648 – 2.815)	0.294	5.618	<0.001
WSR Z	1.561 (1.125 – 2.166)	0.261	2.665	0.008
outgroup [CSU LA]	0.981 (0.691 – 1.394)	0.176	-0.106	0.915
outgroup [UCLA]	0.454 (0.316 – 0.651)	0.084	-4.294	<0.001
desirability Z	1.235 (1.163 – 1.312)	0.038	6.856	<0.001
WSR Z * outgroup [CSU LA]	1.114 (0.692 – 1.796)	0.271	0.445	0.656
WSR Z * outgroup [UCLA]	0.822 (0.511 – 1.323)	0.200	-0.806	0.420
Random Effects
σ2	3.29
τ00 subID	0.66
τ00 trait	0.34
τ11 subID.WSR.Z	1.26
τ11 subID.desirability.Z	0.05
τ11 trait.outgroupCSU LA	0.46
τ11 trait.outgroupUCLA	0.65
ρ01	0.07
	0.59
	-0.89
	-0.16
ICC	0.42
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.066 / 0.459

ggpredict(m, c("WSR.Z", "outgroup")) %>% plot(show.title=F) + xlab("Similarity-Weighted Self-Evaluations") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()

## Data were 'prettified'. Consider using `terms="WSR.Z [all]"` to get smooth plots.

# fullTest$outgroupP <- factor(fullTest$outgroup, levels = c("CSU LA", "Not UCR", "UCLA"))
# contrasts(fullTest$outgroupP) <- contr.poly(3)
# m <- glmer( ingChoiceN ~ WSR.Z * outgroupP + desirability.Z + evalLOO.Z + ( WSR.Z | subID) + ( outgroupP | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
#                                     optCtrl = list(maxfun = 100000)),
#     nAGQ = 1)
# tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")
# ggpredict(m, c("WSR.Z", "outgroup")) %>% plot(show.title=F) + xlab("Similarity-Weighted Self-Evaluations") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()

Do cross-validation predictions predict ingroup choices?

No covariates

m <- glmer( ingChoiceN ~ predicted.Z + ( predicted.Z | subID) + ( outgroup | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.704 (1.478 – 1.966)	0.124	7.329	<0.001
predicted Z	1.581 (1.324 – 1.889)	0.143	5.058	<0.001
Random Effects
σ2	3.29
τ00 subID	0.51
τ00 trait	0.42
τ11 subID.predicted.Z	0.56
τ11 trait.outgroupCSU LA	0.47
τ11 trait.outgroupUCLA	0.69
ρ01 subID	0.28
ρ01 trait.outgroupCSU LA	-0.88
ρ01 trait.outgroupUCLA	-0.14
ICC	0.31
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.042 / 0.341

With covariates

m <- glmer( ingChoiceN ~ predicted.Z + desirability.Z +  ( predicted.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.622 (1.410 – 1.866)	0.116	6.774	<0.001
predicted Z	1.545 (1.287 – 1.855)	0.144	4.661	<0.001
desirability Z	1.260 (1.182 – 1.344)	0.041	7.052	<0.001
Random Effects
σ2	3.29
τ00 subID	0.52
τ00 trait	0.13
τ11 subID.predicted.Z	0.64
ρ01 subID	0.28
ICC	0.28
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.051 / 0.320

Covariates and Condition Differences

No effect

m <- glmer( ingChoiceN ~ predicted.Z*outgroup + desirability.Z + ( predicted.Z | subID) + ( outgroup | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	2.357 (1.877 – 2.960)	0.274	7.382	<0.001
predicted Z	1.466 (1.164 – 1.846)	0.172	3.254	0.001
outgroup [CSU LA]	0.864 (0.641 – 1.164)	0.132	-0.962	0.336
outgroup [UCLA]	0.404 (0.296 – 0.551)	0.064	-5.720	<0.001
desirability Z	1.197 (1.142 – 1.255)	0.029	7.505	<0.001
predicted Z * outgroup [CSU LA]	1.136 (0.790 – 1.633)	0.210	0.689	0.491
predicted Z * outgroup [UCLA]	0.874 (0.610 – 1.252)	0.160	-0.735	0.462
Random Effects
σ2	3.29
τ00 subID	0.43
τ00 trait	0.34
τ11 subID.predicted.Z	0.36
τ11 trait.outgroupCSU LA	0.46
τ11 trait.outgroupUCLA	0.65
ρ01 subID	0.26
ρ01 trait.outgroupCSU LA	-0.90
ρ01 trait.outgroupUCLA	-0.14
ICC	0.27
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.077 / 0.324

ggpredict(m, c("predicted.Z", "outgroup")) %>% plot(show.title=F) + xlab("Cross-Validated Expectations") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()

## Data were 'prettified'. Consider using `terms="predicted.Z [all]"` to get smooth plots.

Does dot-product expected ratings predict ingroup choices?

No covariates

m <- glmer( ingChoiceN ~ er.Z + ( er.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.565 (1.306 – 1.876)	0.145	4.848	<0.001
er Z	1.399 (1.110 – 1.763)	0.165	2.846	0.004
Random Effects
σ2	3.29
τ00 subID	1.01
τ00 trait	0.18
τ11 subID.er.Z	1.95
ρ01 subID	-0.03
ICC	0.49
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.017 / 0.497

With covariates

m <- glmer( ingChoiceN ~ er.Z + desirability.Z +  ( er.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.568 (1.314 – 1.871)	0.141	4.984	<0.001
er Z	1.390 (1.103 – 1.751)	0.164	2.794	0.005
desirability Z	1.264 (1.186 – 1.347)	0.041	7.223	<0.001
Random Effects
σ2	3.29
τ00 subID	1.01
τ00 trait	0.13
τ11 subID.er.Z	1.95
ρ01 subID	-0.03
ICC	0.48
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.026 / 0.497

Covariates and Condition Differences

Marginall difference in CSU LA compared to Not UCR such that effect of expected ratings is slightly stronger on ingroup choices.

m <- glmer( ingChoiceN ~ er.Z*outgroup + desirability.Z + ( er.Z | subID) + ( outgroup | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	2.120 (1.608 – 2.795)	0.299	5.330	<0.001
er Z	1.369 (0.986 – 1.901)	0.229	1.874	0.061
outgroup [CSU LA]	0.949 (0.660 – 1.366)	0.176	-0.280	0.780
outgroup [UCLA]	0.422 (0.291 – 0.613)	0.080	-4.531	<0.001
desirability Z	1.206 (1.150 – 1.264)	0.029	7.755	<0.001
er Z * outgroup [CSU LA]	1.196 (0.746 – 1.919)	0.288	0.743	0.457
er Z * outgroup [UCLA]	0.788 (0.491 – 1.265)	0.190	-0.987	0.324
Random Effects
σ2	3.29
τ00 subID	0.65
τ00 trait	0.34
τ11 subID.er.Z	1.27
τ11 trait.outgroupCSU LA	0.47
τ11 trait.outgroupUCLA	0.65
ρ01 subID	0.02
ρ01 trait.outgroupCSU LA	-0.89
ρ01 trait.outgroupUCLA	-0.14
ICC	0.42
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.054 / 0.447

ggpredict(m, c("er.Z", "outgroup")) %>% plot(show.title=F) + xlab("Dot-Product Expected Rating") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()

## Data were 'prettified'. Consider using `terms="er.Z [all]"` to get smooth plots.

Does the linear trend of self-descriptiveness predict ingroup choices?

No covariates

m <- glmer( ingChoiceN ~ slope.Z + ( slope.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.629 (1.357 – 1.956)	0.152	5.239	<0.001
slope Z	1.517 (1.188 – 1.938)	0.189	3.338	0.001
Random Effects
σ2	3.29
τ00 subID	1.01
τ00 trait	0.18
τ11 subID.slope.Z	2.13
ρ01 subID	-0.07
ICC	0.50
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.026 / 0.515

With covariates

m <- glmer( ingChoiceN ~ slope.Z + desirability.Z +  ( slope.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.632 (1.365 – 1.951)	0.149	5.382	<0.001
slope Z	1.506 (1.180 – 1.922)	0.187	3.286	0.001
desirability Z	1.262 (1.184 – 1.345)	0.041	7.174	<0.001
Random Effects
σ2	3.29
τ00 subID	1.00
τ00 trait	0.13
τ11 subID.slope.Z	2.13
ρ01 subID	-0.07
ICC	0.50
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.034 / 0.514

Covariates and Condition Differences

Effect of self-descriptiveness trend is greatest for UCLA and weakest for CSU LA

m <- glmer( ingChoiceN ~ slope.Z*outgroup + desirability.Z + ( slope.Z | subID) + ( outgroup | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	2.230 (1.699 – 2.925)	0.309	5.788	<0.001
slope Z	1.509 (1.087 – 2.095)	0.252	2.458	0.014
outgroup [CSU LA]	0.918 (0.643 – 1.309)	0.166	-0.474	0.635
outgroup [UCLA]	0.408 (0.283 – 0.587)	0.076	-4.827	<0.001
desirability Z	1.205 (1.149 – 1.264)	0.029	7.729	<0.001
slope Z * outgroup [CSU LA]	1.021 (0.635 – 1.644)	0.248	0.087	0.931
slope Z * outgroup [UCLA]	0.782 (0.486 – 1.259)	0.190	-1.012	0.312
Random Effects
σ2	3.29
τ00 subID	0.59
τ00 trait	0.34
τ11 subID.slope.Z	1.24
τ11 trait.outgroupCSU LA	0.46
τ11 trait.outgroupUCLA	0.65
ρ01 subID	-0.02
ρ01 trait.outgroupCSU LA	-0.89
ρ01 trait.outgroupUCLA	-0.15
ICC	0.41
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.061 / 0.443

ggpredict(m, c("slope.Z", "outgroup")) %>% plot(show.title=F) + xlab("Similarity-Weighted Self-Evaluations") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()

## Data were 'prettified'. Consider using `terms="slope.Z [all]"` to get smooth plots.

Do the neighboring self-evaluations predict ingroup choices?

All neighbors

No covariates

m <- glmer( ingChoiceN ~ neighAveAllSE.Z + ( neighAveAllSE.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.690 (1.473 – 1.939)	0.118	7.491	<0.001
neighAveAllSE Z	1.236 (1.115 – 1.369)	0.065	4.052	<0.001
Random Effects
σ2	3.29
τ00 subID	0.62
τ00 trait	0.18
τ11 subID.neighAveAllSE.Z	0.36
ρ01 subID	-0.07
ICC	0.26
N subID	200
N trait	148
Observations	29329
Marginal R2 / Conditional R2	0.010 / 0.269

With covariates

m <- glmer( ingChoiceN ~ neighAveAllSE.Z + desirability.Z +  ( neighAveAllSE.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.692 (1.483 – 1.930)	0.114	7.807	<0.001
neighAveAllSE Z	1.232 (1.112 – 1.365)	0.064	4.001	<0.001
desirability Z	1.265 (1.187 – 1.347)	0.041	7.281	<0.001
Random Effects
σ2	3.29
τ00 subID	0.62
τ00 trait	0.13
τ11 subID.neighAveAllSE.Z	0.36
ρ01 subID	-0.07
ICC	0.25
N subID	200
N trait	148
Observations	29329
Marginal R2 / Conditional R2	0.023 / 0.269

Covariates and Condition Differences

Effect of neighboring self-evaluations on ingroup choices is strongest for UCLA and weakest for CSU LA

m <- glmer( ingChoiceN ~ neighAveAllSE.Z*outgroup + desirability.Z + ( neighAveAllSE.Z | subID) + ( outgroup | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	2.463 (1.991 – 3.047)	0.267	8.310	<0.001
neighAveAllSE Z	1.225 (1.050 – 1.429)	0.096	2.575	0.010
outgroup [CSU LA]	0.814 (0.612 – 1.082)	0.118	-1.415	0.157
outgroup [UCLA]	0.395 (0.294 – 0.530)	0.059	-6.169	<0.001
desirability Z	1.210 (1.154 – 1.269)	0.029	7.899	<0.001
neighAveAllSE Z * outgroup [CSU LA]	1.019 (0.819 – 1.267)	0.113	0.168	0.867
neighAveAllSE Z * outgroup [UCLA]	0.893 (0.716 – 1.115)	0.101	-0.999	0.318
Random Effects
σ2	3.29
τ00 subID	0.50
τ00 trait	0.33
τ11 subID.neighAveAllSE.Z	0.25
τ11 trait.outgroupCSU LA	0.45
τ11 trait.outgroupUCLA	0.65
ρ01 subID	-0.02
ρ01 trait.outgroupCSU LA	-0.89
ρ01 trait.outgroupUCLA	-0.16
ICC	0.26
N subID	200
N trait	148
Observations	29329
Marginal R2 / Conditional R2	0.052 / 0.299

ggpredict(m, c("neighAveAllSE.Z", "outgroup")) %>% plot(show.title=F) + xlab("Neighbors' Average Self-Descriptiveness") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()

## Data were 'prettified'. Consider using `terms="neighAveAllSE.Z [all]"` to get smooth plots.

All neighbors

No covariates

m <- glmer( ingChoiceN ~ neighAveOutSE.Z + ( neighAveOutSE.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.710 (1.493 – 1.958)	0.118	7.768	<0.001
neighAveOutSE Z	1.197 (1.100 – 1.302)	0.051	4.174	<0.001
Random Effects
σ2	3.29
τ00 subID	0.63
τ00 trait	0.18
τ11 subID.neighAveOutSE.Z	0.24
ρ01 subID	-0.08
ICC	0.24
N subID	200
N trait	147
Observations	29060
Marginal R2 / Conditional R2	0.007 / 0.248

With covariates

m <- glmer( ingChoiceN ~ neighAveOutSE.Z + desirability.Z +  ( neighAveOutSE.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.710 (1.502 – 1.947)	0.113	8.100	<0.001
neighAveOutSE Z	1.195 (1.098 – 1.300)	0.051	4.145	<0.001
desirability Z	1.265 (1.188 – 1.347)	0.041	7.330	<0.001
Random Effects
σ2	3.29
τ00 subID	0.63
τ00 trait	0.13
τ11 subID.neighAveOutSE.Z	0.24
ρ01 subID	-0.08
ICC	0.23
N subID	200
N trait	147
Observations	29060
Marginal R2 / Conditional R2	0.021 / 0.249

Covariates and Condition Differences

Effect of neighboring self-evaluations on ingroup choices is strongest for UCLA and weakest for CSU LA

m <- glmer( ingChoiceN ~ neighAveOutSE.Z*outgroup + desirability.Z + ( neighAveOutSE.Z | subID) + ( outgroup | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	2.523 (2.048 – 3.109)	0.269	8.689	<0.001
neighAveOutSE Z	1.151 (1.013 – 1.307)	0.075	2.161	0.031
outgroup [CSU LA]	0.804 (0.606 – 1.066)	0.116	-1.516	0.130
outgroup [UCLA]	0.382 (0.285 – 0.512)	0.057	-6.459	<0.001
desirability Z	1.209 (1.154 – 1.268)	0.029	7.894	<0.001
neighAveOutSE Z * outgroup [CSU LA]	1.086 (0.909 – 1.298)	0.099	0.908	0.364
neighAveOutSE Z * outgroup [UCLA]	0.943 (0.786 – 1.130)	0.087	-0.637	0.524
Random Effects
σ2	3.29
τ00 subID	0.51
τ00 trait	0.33
τ11 subID.neighAveOutSE.Z	0.16
τ11 trait.outgroupCSU LA	0.45
τ11 trait.outgroupUCLA	0.63
ρ01 subID	-0.05
ρ01 trait.outgroupCSU LA	-0.89
ρ01 trait.outgroupUCLA	-0.15
ICC	0.25
N subID	200
N trait	147
Observations	29060
Marginal R2 / Conditional R2	0.052 / 0.287

ggpredict(m, c("neighAveOutSE.Z", "outgroup")) %>% plot(show.title=F) + xlab("Outwards Neighbors' Average Self-Descriptiveness") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()

## Data were 'prettified'. Consider using `terms="neighAveOutSE.Z [all]"` to get smooth plots.

Indegree neighbors

No covariates

m <- glmer( ingChoiceN ~ neighAveInSE.Z + ( neighAveInSE.Z | subID) + ( outgroup | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.777 (1.549 – 2.038)	0.124	8.208	<0.001
neighAveInSE Z	1.148 (1.064 – 1.238)	0.044	3.575	<0.001
Random Effects
σ2	3.29
τ00 subID	0.64
τ00 trait	0.43
τ11 subID.neighAveInSE.Z	0.16
τ11 trait.outgroupCSU LA	0.46
τ11 trait.outgroupUCLA	0.70
ρ01 subID	0.15
ρ01 trait.outgroupCSU LA	-0.87
ρ01 trait.outgroupUCLA	-0.15
ICC	0.27
N subID	200
N trait	148
Observations	29282
Marginal R2 / Conditional R2	0.004 / 0.276

With covariates

m <- glmer( ingChoiceN ~ neighAveInSE.Z + desirability.Z +  ( neighAveInSE.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.699 (1.488 – 1.940)	0.115	7.839	<0.001
neighAveInSE Z	1.158 (1.068 – 1.254)	0.047	3.569	<0.001
desirability Z	1.264 (1.186 – 1.348)	0.041	7.176	<0.001
Random Effects
σ2	3.29
τ00 subID	0.65
τ00 trait	0.13
τ11 subID.neighAveInSE.Z	0.20
ρ01 subID	0.06
ICC	0.23
N subID	200
N trait	148
Observations	29282
Marginal R2 / Conditional R2	0.019 / 0.246

Covariates and Condition Differences

Effect of neighboring self-evaluations on ingroup choices is strongest for UCLA and weakest for CSU LA

m <- glmer( ingChoiceN ~ neighAveInSE.Z*outgroup + desirability.Z + ( neighAveInSE.Z | subID) + ( outgroup | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	2.498 (2.018 – 3.093)	0.272	8.402	<0.001
neighAveInSE Z	1.152 (1.014 – 1.309)	0.075	2.167	0.030
outgroup [CSU LA]	0.820 (0.614 – 1.094)	0.121	-1.352	0.176
outgroup [UCLA]	0.384 (0.285 – 0.517)	0.058	-6.294	<0.001
desirability Z	1.206 (1.150 – 1.264)	0.029	7.759	<0.001
neighAveInSE Z * outgroup [CSU LA]	1.015 (0.849 – 1.214)	0.093	0.168	0.866
neighAveInSE Z * outgroup [UCLA]	0.941 (0.785 – 1.129)	0.087	-0.652	0.514
Random Effects
σ2	3.29
τ00 subID	0.54
τ00 trait	0.34
τ11 subID.neighAveInSE.Z	0.16
τ11 trait.outgroupCSU LA	0.46
τ11 trait.outgroupUCLA	0.65
ρ01 subID	0.12
ρ01 trait.outgroupCSU LA	-0.90
ρ01 trait.outgroupUCLA	-0.16
ICC	0.25
N subID	200
N trait	148
Observations	29282
Marginal R2 / Conditional R2	0.050 / 0.291

ggpredict(m, c("neighAveInSE.Z", "outgroup")) %>% plot(show.title=F) + xlab("Inwards Neighbors' Average Self-Descriptiveness") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()

## Data were 'prettified'. Consider using `terms="neighAveInSE.Z [all]"` to get smooth plots.

Do people self-anchor more for higher indegree traits?

Similarity-Weighted Self-Evaluations

m <- glmer( ingChoiceN ~ WSR.Z * inDegree.Z + desirability.Z + ( WSR.Z + inDegree.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.664 (1.371 – 2.020)	0.164	5.153	<0.001
WSR Z	1.858 (1.423 – 2.426)	0.253	4.554	<0.001
inDegree Z	1.003 (0.941 – 1.069)	0.033	0.092	0.927
desirability Z	1.249 (1.173 – 1.330)	0.040	6.938	<0.001
WSR Z * inDegree Z	1.037 (1.006 – 1.068)	0.016	2.345	0.019
Random Effects
σ2	3.29
τ00 subID	1.27
τ00 trait	0.12
τ11 subID.WSR.Z	2.61
τ11 subID.inDegree.Z	0.01
ρ01 subID.WSR.Z	0.07
ρ01 subID.inDegree.Z	0.18
ICC	0.55
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.058 / 0.575

ggpredict(m, c("WSR.Z", "inDegree.Z")) %>% plot(show.title=F) + xlab("Similarity-Weighted Self-Descriptiveness") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()

## Data were 'prettified'. Consider using `terms="WSR.Z [all]"` to get smooth plots.

Cross-Validated Expectations

m <- glmer( ingChoiceN ~ predicted.Z * inDegree.Z + desirability.Z + ( predicted.Z + inDegree.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.649 (1.436 – 1.894)	0.116	7.087	<0.001
predicted Z	1.550 (1.292 – 1.860)	0.144	4.710	<0.001
inDegree Z	1.001 (0.936 – 1.071)	0.034	0.039	0.969
desirability Z	1.261 (1.181 – 1.347)	0.042	6.951	<0.001
predicted Z * inDegree Z	1.034 (1.004 – 1.064)	0.016	2.194	0.028
Random Effects
σ2	3.29
τ00 subID	0.51
τ00 trait	0.13
τ11 subID.predicted.Z	0.62
τ11 subID.inDegree.Z	0.01
ρ01 subID.predicted.Z	0.28
ρ01 subID.inDegree.Z	0.33
ICC	0.28
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.053 / 0.316

Dot-Product Expected Ratings

m <- glmer( ingChoiceN ~ er.Z * inDegree.Z + desirability.Z + ( er.Z + inDegree.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.585 (1.328 – 1.891)	0.143	5.108	<0.001
er Z	1.400 (1.111 – 1.765)	0.165	2.849	0.004
inDegree Z	1.009 (0.944 – 1.078)	0.034	0.261	0.794
desirability Z	1.263 (1.184 – 1.347)	0.042	7.066	<0.001
er Z * inDegree Z	1.036 (1.006 – 1.067)	0.016	2.355	0.019
Random Effects
σ2	3.29
τ00 subID	1.00
τ00 trait	0.13
τ11 subID.er.Z	1.96
τ11 subID.inDegree.Z	0.01
ρ01 subID.er.Z	-0.02
ρ01 subID.inDegree.Z	0.16
ICC	0.48
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.027 / 0.498

Linear Trend of Self-Descriptiveness

m <- glmer( ingChoiceN ~ slope.Z * inDegree.Z + desirability.Z + ( slope.Z + inDegree.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.653 (1.384 – 1.976)	0.150	5.532	<0.001
slope Z	1.514 (1.185 – 1.934)	0.189	3.318	0.001
inDegree Z	1.009 (0.944 – 1.078)	0.034	0.260	0.795
desirability Z	1.261 (1.182 – 1.345)	0.042	7.021	<0.001
slope Z * inDegree Z	1.038 (1.008 – 1.070)	0.016	2.451	0.014
Random Effects
σ2	3.29
τ00 subID	1.00
τ00 trait	0.13
τ11 subID.slope.Z	2.14
τ11 subID.inDegree.Z	0.01
ρ01 subID.slope.Z	-0.06
ρ01 subID.inDegree.Z	0.22
ICC	0.50
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.035 / 0.515

All Neighboring Self-Evaluations

m <- glmer( ingChoiceN ~ neighAveAllSE.Z * inDegree.Z + desirability.Z + ( neighAveAllSE.Z + inDegree.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.697 (1.487 – 1.936)	0.114	7.864	<0.001
neighAveAllSE Z	1.249 (1.127 – 1.384)	0.065	4.241	<0.001
inDegree Z	1.012 (0.948 – 1.081)	0.034	0.365	0.715
desirability Z	1.262 (1.184 – 1.346)	0.041	7.121	<0.001
neighAveAllSE Z * inDegree Z	1.043 (1.012 – 1.074)	0.016	2.718	0.007
Random Effects
σ2	3.29
τ00 subID	0.62
τ00 trait	0.13
τ11 subID.neighAveAllSE.Z	0.35
τ11 subID.inDegree.Z	0.01
ρ01 subID.neighAveAllSE.Z	-0.07
ρ01 subID.inDegree.Z	0.35
ICC	0.25
N subID	200
N trait	148
Observations	29329
Marginal R2 / Conditional R2	0.025 / 0.271

Outwards Neighboring Self-Evaluations

m <- glmer( ingChoiceN ~ neighAveOutSE.Z * inDegree.Z + desirability.Z + ( neighAveOutSE.Z + inDegree.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.713 (1.504 – 1.950)	0.114	8.117	<0.001
neighAveOutSE Z	1.201 (1.104 – 1.306)	0.052	4.249	<0.001
inDegree Z	1.011 (0.947 – 1.080)	0.034	0.337	0.736
desirability Z	1.263 (1.185 – 1.347)	0.041	7.170	<0.001
neighAveOutSE Z * inDegree Z	1.029 (0.999 – 1.059)	0.015	1.891	0.059
Random Effects
σ2	3.29
τ00 subID	0.63
τ00 trait	0.13
τ11 subID.neighAveOutSE.Z	0.24
τ11 subID.inDegree.Z	0.01
ρ01 subID.neighAveOutSE.Z	-0.08
ρ01 subID.inDegree.Z	0.32
ICC	0.23
N subID	200
N trait	147
Observations	29060
Marginal R2 / Conditional R2	0.022 / 0.251

Inwards Neighboring Self-Evaluations

m <- glmer( ingChoiceN ~ neighAveInSE.Z * inDegree.Z + desirability.Z + ( neighAveInSE.Z + inDegree.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.703 (1.492 – 1.945)	0.115	7.879	<0.001
neighAveInSE Z	1.186 (1.093 – 1.287)	0.049	4.099	<0.001
inDegree Z	1.018 (0.953 – 1.088)	0.034	0.535	0.593
desirability Z	1.260 (1.181 – 1.344)	0.042	6.986	<0.001
neighAveInSE Z * inDegree Z	1.051 (1.019 – 1.084)	0.017	3.142	0.002
Random Effects
σ2	3.29
τ00 subID	0.66
τ00 trait	0.13
τ11 subID.neighAveInSE.Z	0.20
τ11 subID.inDegree.Z	0.01
ρ01 subID.neighAveInSE.Z	0.06
ρ01 subID.inDegree.Z	0.34
ICC	0.23
N subID	200
N trait	148
Observations	29282
Marginal R2 / Conditional R2	0.021 / 0.248

Do people self-anchor more for higher outdegree traits?

Similarity-Weighted Self-Evaluations

Similarity-weighted self-descriptiveness more predictive of ingroup choices for higher outdegree traits

m <- glmer( ingChoiceN ~ WSR.Z * outDegree.Z + desirability.Z + ( WSR.Z + outDegree.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.689 (1.400 – 2.036)	0.161	5.481	<0.001
WSR Z	1.818 (1.409 – 2.345)	0.236	4.595	<0.001
outDegree Z	1.097 (1.017 – 1.182)	0.042	2.405	0.016
desirability Z	1.215 (1.134 – 1.301)	0.043	5.528	<0.001
WSR Z * outDegree Z	1.076 (1.034 – 1.121)	0.022	3.567	<0.001
Random Effects
σ2	3.29
τ00 subID	1.19
τ00 trait	0.12
τ11 subID.WSR.Z	2.31
τ11 subID.outDegree.Z	0.04
ρ01 subID.WSR.Z	0.04
ρ01 subID.outDegree.Z	0.48
ICC	0.53
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.059 / 0.554

ggpredict(m, c("WSR.Z", "outDegree.Z")) %>% plot(show.title=F) + xlab("Similarity-Weighted Self-Descriptiveness") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()

## Data were 'prettified'. Consider using `terms="WSR.Z [all]"` to get smooth plots.

Cross-Validated Expectations

m <- glmer( ingChoiceN ~ predicted.Z * outDegree.Z + desirability.Z + ( predicted.Z + outDegree.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.699 (1.485 – 1.943)	0.116	7.740	<0.001
predicted Z	1.501 (1.297 – 1.738)	0.112	5.432	<0.001
outDegree Z	1.103 (1.018 – 1.195)	0.045	2.399	0.016
desirability Z	1.231 (1.145 – 1.323)	0.045	5.645	<0.001
predicted Z * outDegree Z	1.086 (1.041 – 1.134)	0.024	3.771	<0.001
Random Effects
σ2	3.29
τ00 subID	0.59
τ00 trait	0.13
τ11 subID.predicted.Z	0.23
τ11 subID.outDegree.Z	0.05
ρ01 subID.predicted.Z	0.13
ρ01 subID.outDegree.Z	0.86
ICC	0.23
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.056 / 0.275

Dot-Product Expected Ratings

m <- glmer( ingChoiceN ~ er.Z * outDegree.Z + desirability.Z + ( er.Z + outDegree.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.616 (1.362 – 1.917)	0.141	5.507	<0.001
er Z	1.389 (1.114 – 1.731)	0.156	2.925	0.003
outDegree Z	1.101 (1.019 – 1.190)	0.044	2.432	0.015
desirability Z	1.228 (1.144 – 1.318)	0.045	5.657	<0.001
er Z * outDegree Z	1.066 (1.024 – 1.109)	0.022	3.141	0.002
Random Effects
σ2	3.29
τ00 subID	0.94
τ00 trait	0.13
τ11 subID.er.Z	1.72
τ11 subID.outDegree.Z	0.04
ρ01 subID.er.Z	-0.05
ρ01 subID.outDegree.Z	0.55
ICC	0.46
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.030 / 0.478

Linear Trend of Self-Descriptiveness

m <- glmer( ingChoiceN ~ slope.Z * outDegree.Z + desirability.Z + ( slope.Z + outDegree.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.677 (1.412 – 1.993)	0.148	5.879	<0.001
slope Z	1.497 (1.186 – 1.890)	0.178	3.399	0.001
outDegree Z	1.102 (1.020 – 1.191)	0.044	2.453	0.014
desirability Z	1.225 (1.141 – 1.315)	0.044	5.615	<0.001
slope Z * outDegree Z	1.074 (1.031 – 1.118)	0.022	3.456	0.001
Random Effects
σ2	3.29
τ00 subID	0.94
τ00 trait	0.13
τ11 subID.slope.Z	1.88
τ11 subID.outDegree.Z	0.04
ρ01 subID.slope.Z	-0.10
ρ01 subID.outDegree.Z	0.56
ICC	0.48
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.038 / 0.495

All Neighboring Self-Evaluations

m <- glmer( ingChoiceN ~ neighAveAllSE.Z * outDegree.Z + desirability.Z + ( neighAveAllSE.Z + outDegree.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.720 (1.506 – 1.965)	0.117	7.997	<0.001
neighAveAllSE Z	1.267 (1.147 – 1.401)	0.065	4.644	<0.001
outDegree Z	1.121 (1.038 – 1.212)	0.044	2.901	0.004
desirability Z	1.222 (1.139 – 1.311)	0.044	5.614	<0.001
neighAveAllSE Z * outDegree Z	1.073 (1.033 – 1.115)	0.021	3.598	<0.001
Random Effects
σ2	3.29
τ00 subID	0.65
τ00 trait	0.12
τ11 subID.neighAveAllSE.Z	0.31
τ11 subID.outDegree.Z	0.05
ρ01 subID.neighAveAllSE.Z	-0.05
ρ01 subID.outDegree.Z	0.76
ICC	0.26
N subID	200
N trait	148
Observations	29329
Marginal R2 / Conditional R2	0.030 / 0.279

Outwards Neighboring Self-Evaluations

m <- glmer( ingChoiceN ~ neighAveOutSE.Z * outDegree.Z + desirability.Z + ( neighAveOutSE.Z + outDegree.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.738 (1.525 – 1.982)	0.116	8.273	<0.001
neighAveOutSE Z	1.234 (1.133 – 1.343)	0.054	4.845	<0.001
outDegree Z	1.109 (1.025 – 1.199)	0.044	2.590	0.010
desirability Z	1.228 (1.145 – 1.317)	0.044	5.769	<0.001
neighAveOutSE Z * outDegree Z	1.067 (1.026 – 1.110)	0.021	3.221	0.001
Random Effects
σ2	3.29
τ00 subID	0.65
τ00 trait	0.12
τ11 subID.neighAveOutSE.Z	0.22
τ11 subID.outDegree.Z	0.05
ρ01 subID.neighAveOutSE.Z	-0.09
ρ01 subID.outDegree.Z	0.78
ICC	0.24
N subID	200
N trait	147
Observations	29060
Marginal R2 / Conditional R2	0.028 / 0.262

Inwards Neighboring Self-Evaluations

m <- glmer( ingChoiceN ~ neighAveInSE.Z * outDegree.Z + desirability.Z + ( neighAveInSE.Z + outDegree.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.731 (1.512 – 1.981)	0.119	7.969	<0.001
neighAveInSE Z	1.169 (1.081 – 1.265)	0.047	3.895	<0.001
outDegree Z	1.120 (1.035 – 1.212)	0.045	2.802	0.005
desirability Z	1.224 (1.140 – 1.314)	0.044	5.557	<0.001
neighAveInSE Z * outDegree Z	1.053 (1.016 – 1.091)	0.019	2.857	0.004
Random Effects
σ2	3.29
τ00 subID	0.69
τ00 trait	0.13
τ11 subID.neighAveInSE.Z	0.18
τ11 subID.outDegree.Z	0.05
ρ01 subID.neighAveInSE.Z	0.06
ρ01 subID.outDegree.Z	0.81
ICC	0.24
N subID	200
N trait	148
Observations	29282
Marginal R2 / Conditional R2	0.024 / 0.262

Do cross-validated similarity * self-evaluation predictions predict ingroup choices, regardless of whether it was seen prior or not?

Similarity-Weighted Self-Evaluations

m <- glmer( ingChoiceN ~ WSR.Z * novel + desirability.Z + ( WSR.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)

## boundary (singular) fit: see help('isSingular')

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.651 (1.360 – 2.004)	0.163	5.065	<0.001
WSR Z	1.874 (1.436 – 2.446)	0.255	4.627	<0.001
novel [Held Out]	0.983 (0.932 – 1.036)	0.027	-0.643	0.520
desirability Z	1.249 (1.174 – 1.329)	0.039	7.064	<0.001
WSR Z * novel [Held Out]	0.965 (0.913 – 1.019)	0.027	-1.287	0.198
Random Effects
σ2	3.29
τ00 subID	1.26
τ00 trait	0.12
τ11 subID.WSR.Z	2.59
τ11 subID.novelHeld Out	0.00
ρ01 subID.WSR.Z	0.06
ρ01 subID.novelHeld Out	0.80
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.117 / NA

ggpredict(m, c("WSR.Z", "novel")) %>% plot(show.title=F) + xlab("Similarity-Weighted Self-Descriptiveness") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()

## Data were 'prettified'. Consider using `terms="WSR.Z [all]"` to get smooth plots.

Moderated by outgroup comparison

m <- glmer( ingChoiceN ~ WSR.Z * novel * outgroup + desirability.Z + ( WSR.Z + novel | subID) + ( outgroup | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)

## boundary (singular) fit: see help('isSingular')

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	2.199 (1.637 – 2.953)	0.331	5.233	<0.001
WSR Z	1.705 (1.190 – 2.443)	0.313	2.905	0.004
novel [Held Out]	0.962 (0.872 – 1.062)	0.048	-0.769	0.442
outgroup [CSU LA]	0.917 (0.621 – 1.353)	0.182	-0.438	0.661
outgroup [UCLA]	0.444 (0.298 – 0.661)	0.090	-4.002	<0.001
desirability Z	1.197 (1.142 – 1.254)	0.029	7.514	<0.001
WSR Z * novel [Held Out]	0.960 (0.882 – 1.044)	0.041	-0.959	0.338
WSR Z * outgroup [CSU LA]	1.086 (0.646 – 1.824)	0.287	0.310	0.756
WSR Z * outgroup [UCLA]	0.834 (0.494 – 1.407)	0.223	-0.681	0.496
novel [Held Out] * outgroup [CSU LA]	0.986 (0.863 – 1.126)	0.067	-0.214	0.831
novel [Held Out] * outgroup [UCLA]	1.095 (0.955 – 1.256)	0.076	1.302	0.193
(WSR Z * novel [Held Out]) * outgroup [CSU LA]	0.985 (0.862 – 1.125)	0.067	-0.224	0.823
(WSR Z * novel [Held Out]) * outgroup [UCLA]	1.058 (0.926 – 1.210)	0.072	0.830	0.407
Random Effects
σ2	3.29
τ00 subID	0.75
τ00 trait	0.34
τ11 subID.WSR.Z	1.56
τ11 subID.novelHeld Out	0.00
τ11 trait.outgroupCSU LA	0.47
τ11 trait.outgroupUCLA	0.63
ρ01	0.11
	0.59
	-0.89
	-0.16
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.119 / NA

ggpredict(m, c("WSR.Z", "novel", "outgroup")) %>% plot(show.title=F) + xlab("Similarity-Weighted Self-Descriptiveness") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()

## Data were 'prettified'. Consider using `terms="WSR.Z [all]"` to get smooth plots.

Cross-Validated Expectations

m <- glmer( ingChoiceN ~ predicted.Z * novel + desirability.Z + ( predicted.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)

## boundary (singular) fit: see help('isSingular')

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.632 (1.416 – 1.880)	0.118	6.767	<0.001
predicted Z	1.573 (1.309 – 1.891)	0.148	4.822	<0.001
novel [Held Out]	0.988 (0.937 – 1.042)	0.027	-0.448	0.654
desirability Z	1.260 (1.182 – 1.344)	0.041	7.055	<0.001
predicted Z * novel [Held Out]	0.962 (0.912 – 1.016)	0.026	-1.397	0.162
Random Effects
σ2	3.29
τ00 subID	0.53
τ00 trait	0.13
τ11 subID.predicted.Z	0.64
τ11 subID.novelHeld Out	0.00
ρ01 subID.predicted.Z	0.25
ρ01 subID.novelHeld Out	-0.39
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.071 / NA

Dot-Product Expected Ratings

m <- glmer( ingChoiceN ~ er.Z * novel + desirability.Z + ( er.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)

## boundary (singular) fit: see help('isSingular')

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.581 (1.322 – 1.891)	0.144	5.018	<0.001
er Z	1.409 (1.117 – 1.776)	0.167	2.895	0.004
novel [Held Out]	0.979 (0.928 – 1.032)	0.026	-0.791	0.429
desirability Z	1.264 (1.187 – 1.347)	0.041	7.229	<0.001
er Z * novel [Held Out]	0.962 (0.912 – 1.015)	0.026	-1.409	0.159
Random Effects
σ2	3.29
τ00 subID	1.02
τ00 trait	0.13
τ11 subID.er.Z	1.95
τ11 subID.novelHeld Out	0.00
ρ01 subID.er.Z	-0.01
ρ01 subID.novelHeld Out	-0.42
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.048 / NA

Linear Trend of Self-Descriptiveness

m <- glmer( ingChoiceN ~ slope.Z * novel + desirability.Z + ( slope.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)

## boundary (singular) fit: see help('isSingular')

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.645 (1.373 – 1.970)	0.151	5.406	<0.001
slope Z	1.526 (1.194 – 1.950)	0.191	3.378	0.001
novel [Held Out]	0.980 (0.930 – 1.033)	0.026	-0.743	0.457
desirability Z	1.262 (1.184 – 1.345)	0.041	7.180	<0.001
slope Z * novel [Held Out]	0.960 (0.910 – 1.013)	0.026	-1.479	0.139
Random Effects
σ2	3.29
τ00 subID	1.02
τ00 trait	0.13
τ11 subID.slope.Z	2.13
τ11 subID.novelHeld Out	0.00
ρ01 subID.slope.Z	-0.06
ρ01 subID.novelHeld Out	-0.50
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.064 / NA

All Neighboring Self-Evaluations

m <- glmer( ingChoiceN ~ neighAveAllSE.Z * novel + desirability.Z + ( neighAveAllSE.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)

## boundary (singular) fit: see help('isSingular')

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.701 (1.487 – 1.945)	0.116	7.756	<0.001
neighAveAllSE Z	1.241 (1.118 – 1.378)	0.066	4.052	<0.001
novel [Held Out]	0.988 (0.937 – 1.042)	0.027	-0.450	0.653
desirability Z	1.265 (1.187 – 1.347)	0.041	7.282	<0.001
neighAveAllSE Z * novel [Held Out]	0.983 (0.931 – 1.037)	0.027	-0.629	0.530
Random Effects
σ2	3.29
τ00 subID	0.63
τ00 trait	0.13
τ11 subID.neighAveAllSE.Z	0.36
τ11 subID.novelHeld Out	0.00
ρ01 subID.neighAveAllSE.Z	-0.08
ρ01 subID.novelHeld Out	-0.56
N subID	200
N trait	148
Observations	29329
Marginal R2 / Conditional R2	0.031 / NA

Outwards Neighboring Self-Evaluations

m <- glmer( ingChoiceN ~ neighAveOutSE.Z * novel + desirability.Z + ( neighAveOutSE.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)

## boundary (singular) fit: see help('isSingular')

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.724 (1.509 – 1.968)	0.117	8.038	<0.001
neighAveOutSE Z	1.204 (1.104 – 1.314)	0.053	4.190	<0.001
novel [Held Out]	0.981 (0.930 – 1.035)	0.027	-0.687	0.492
desirability Z	1.265 (1.188 – 1.347)	0.041	7.333	<0.001
neighAveOutSE Z * novel [Held Out]	0.980 (0.928 – 1.035)	0.027	-0.732	0.464
Random Effects
σ2	3.29
τ00 subID	0.64
τ00 trait	0.13
τ11 subID.neighAveOutSE.Z	0.24
τ11 subID.novelHeld Out	0.00
ρ01 subID.neighAveOutSE.Z	-0.07
ρ01 subID.novelHeld Out	-1.00
N subID	200
N trait	147
Observations	29060
Marginal R2 / Conditional R2	0.028 / NA

Inwards Neighboring Self-Evaluations

m <- glmer( ingChoiceN ~ neighAveInSE.Z * novel + desirability.Z + ( neighAveInSE.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)

## boundary (singular) fit: see help('isSingular')

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.711 (1.495 – 1.959)	0.118	7.778	<0.001
neighAveInSE Z	1.168 (1.074 – 1.269)	0.050	3.647	<0.001
novel [Held Out]	0.984 (0.933 – 1.038)	0.027	-0.581	0.561
desirability Z	1.264 (1.186 – 1.348)	0.041	7.177	<0.001
neighAveInSE Z * novel [Held Out]	0.980 (0.928 – 1.034)	0.027	-0.749	0.454
Random Effects
σ2	3.29
τ00 subID	0.67
τ00 trait	0.13
τ11 subID.neighAveInSE.Z	0.20
τ11 subID.novelHeld Out	0.00
ρ01 subID.neighAveInSE.Z	0.05
ρ01 subID.novelHeld Out	-0.61
N subID	200
N trait	148
Observations	29282
Marginal R2 / Conditional R2	0.024 / NA

Does generalization depend on outdegree?

Similarity-Weighted Self-Evaluations

m <- glmer( ingChoiceN ~ WSR.Z * novel * outDegree.Z + desirability.Z + ( WSR.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)

## boundary (singular) fit: see help('isSingular')

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.674 (1.379 – 2.031)	0.165	5.213	<0.001
WSR Z	1.895 (1.452 – 2.472)	0.257	4.710	<0.001
novel [Held Out]	0.984 (0.933 – 1.038)	0.027	-0.595	0.552
outDegree Z	1.058 (0.985 – 1.137)	0.039	1.542	0.123
desirability Z	1.213 (1.133 – 1.299)	0.042	5.549	<0.001
WSR Z * novel [Held Out]	0.963 (0.912 – 1.018)	0.027	-1.326	0.185
WSR Z * outDegree Z	1.070 (1.033 – 1.109)	0.019	3.790	<0.001
novel [Held Out] * outDegree Z	1.025 (0.971 – 1.082)	0.028	0.896	0.370
(WSR Z * novel [Held Out]) * outDegree Z	0.962 (0.910 – 1.016)	0.027	-1.388	0.165
Random Effects
σ2	3.29
τ00 subID	1.26
τ00 trait	0.12
τ11 subID.WSR.Z	2.58
τ11 subID.novelHeld Out	0.00
ρ01 subID.WSR.Z	0.06
ρ01 subID.novelHeld Out	0.81
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.122 / NA

ggpredict(m, c("WSR.Z", "novel")) %>% plot(show.title=F) + xlab("Similarity-Weighted Self-Descriptiveness") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()

## Data were 'prettified'. Consider using `terms="WSR.Z [all]"` to get smooth plots.

Cross-Validated Expectations

m <- glmer( ingChoiceN ~ predicted.Z * novel * outDegree.Z + desirability.Z + ( predicted.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)

## boundary (singular) fit: see help('isSingular')

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.652 (1.435 – 1.902)	0.119	6.975	<0.001
predicted Z	1.565 (1.303 – 1.881)	0.147	4.783	<0.001
novel [Held Out]	0.989 (0.938 – 1.043)	0.027	-0.401	0.689
outDegree Z	1.049 (0.973 – 1.131)	0.040	1.254	0.210
desirability Z	1.230 (1.145 – 1.321)	0.045	5.687	<0.001
predicted Z * novel [Held Out]	0.961 (0.911 – 1.014)	0.026	-1.450	0.147
predicted Z * outDegree Z	1.070 (1.034 – 1.108)	0.019	3.827	<0.001
novel [Held Out] * outDegree Z	1.019 (0.967 – 1.075)	0.028	0.709	0.478
(predicted Z * novel [Held Out]) * outDegree Z	0.961 (0.910 – 1.014)	0.027	-1.449	0.147
Random Effects
σ2	3.29
τ00 subID	0.52
τ00 trait	0.13
τ11 subID.predicted.Z	0.64
τ11 subID.novelHeld Out	0.00
ρ01 subID.predicted.Z	0.27
ρ01 subID.novelHeld Out	-0.44
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.072 / NA

Dot-Product Expected Ratings

m <- glmer( ingChoiceN ~ er.Z * novel * outDegree.Z + desirability.Z + ( er.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)

## boundary (singular) fit: see help('isSingular')

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.598 (1.337 – 1.909)	0.145	5.150	<0.001
er Z	1.423 (1.129 – 1.794)	0.168	2.986	0.003
novel [Held Out]	0.980 (0.929 – 1.033)	0.027	-0.750	0.453
outDegree Z	1.062 (0.986 – 1.143)	0.040	1.591	0.112
desirability Z	1.227 (1.144 – 1.316)	0.044	5.695	<0.001
er Z * novel [Held Out]	0.960 (0.910 – 1.013)	0.026	-1.475	0.140
er Z * outDegree Z	1.071 (1.034 – 1.109)	0.019	3.852	<0.001
novel [Held Out] * outDegree Z	1.022 (0.968 – 1.078)	0.028	0.782	0.434
(er Z * novel [Held Out]) * outDegree Z	0.960 (0.908 – 1.014)	0.027	-1.465	0.143
Random Effects
σ2	3.29
τ00 subID	1.02
τ00 trait	0.13
τ11 subID.er.Z	1.94
τ11 subID.novelHeld Out	0.00
ρ01 subID.er.Z	-0.01
ρ01 subID.novelHeld Out	-0.40
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.052 / NA

Linear Trend of Self-Descriptiveness

m <- glmer( ingChoiceN ~ slope.Z * novel * outDegree.Z + desirability.Z + ( slope.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)

## boundary (singular) fit: see help('isSingular')

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.664 (1.390 – 1.992)	0.153	5.553	<0.001
slope Z	1.540 (1.206 – 1.968)	0.192	3.458	0.001
novel [Held Out]	0.981 (0.930 – 1.035)	0.027	-0.703	0.482
outDegree Z	1.063 (0.987 – 1.144)	0.040	1.623	0.105
desirability Z	1.224 (1.141 – 1.313)	0.044	5.646	<0.001
slope Z * novel [Held Out]	0.958 (0.908 – 1.012)	0.026	-1.534	0.125
slope Z * outDegree Z	1.071 (1.034 – 1.109)	0.019	3.826	<0.001
novel [Held Out] * outDegree Z	1.020 (0.967 – 1.076)	0.028	0.722	0.470
(slope Z * novel [Held Out]) * outDegree Z	0.962 (0.910 – 1.016)	0.027	-1.379	0.168
Random Effects
σ2	3.29
τ00 subID	1.01
τ00 trait	0.12
τ11 subID.slope.Z	2.12
τ11 subID.novelHeld Out	0.00
ρ01 subID.slope.Z	-0.05
ρ01 subID.novelHeld Out	-0.49
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.068 / NA

All Neighboring Self-Evaluations

m <- glmer( ingChoiceN ~ neighAveAllSE.Z * novel * outDegree.Z + desirability.Z + ( neighAveAllSE.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)

## boundary (singular) fit: see help('isSingular')

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.702 (1.490 – 1.945)	0.116	7.818	<0.001
neighAveAllSE Z	1.279 (1.151 – 1.420)	0.068	4.594	<0.001
novel [Held Out]	0.987 (0.936 – 1.041)	0.027	-0.475	0.635
outDegree Z	1.072 (0.997 – 1.153)	0.040	1.869	0.062
desirability Z	1.222 (1.140 – 1.309)	0.043	5.651	<0.001
neighAveAllSE Z * novel [Held Out]	0.975 (0.923 – 1.029)	0.027	-0.920	0.357
neighAveAllSE Z * outDegree Z	1.089 (1.050 – 1.130)	0.020	4.593	<0.001
novel [Held Out] * outDegree Z	1.013 (0.960 – 1.069)	0.028	0.470	0.638
(neighAveAllSE Z * novel [Held Out]) * outDegree Z	0.946 (0.894 – 1.001)	0.027	-1.922	0.055
Random Effects
σ2	3.29
τ00 subID	0.63
τ00 trait	0.12
τ11 subID.neighAveAllSE.Z	0.36
τ11 subID.novelHeld Out	0.00
ρ01 subID.neighAveAllSE.Z	-0.07
ρ01 subID.novelHeld Out	-0.57
N subID	200
N trait	148
Observations	29329
Marginal R2 / Conditional R2	0.036 / NA

Outwards Neighboring Self-Evaluations

m <- glmer( ingChoiceN ~ neighAveOutSE.Z * novel * outDegree.Z + desirability.Z + ( neighAveOutSE.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)

## boundary (singular) fit: see help('isSingular')

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.722 (1.510 – 1.964)	0.116	8.094	<0.001
neighAveOutSE Z	1.252 (1.146 – 1.368)	0.056	4.989	<0.001
novel [Held Out]	0.982 (0.931 – 1.036)	0.027	-0.662	0.508
outDegree Z	1.054 (0.979 – 1.134)	0.039	1.395	0.163
desirability Z	1.228 (1.146 – 1.315)	0.043	5.814	<0.001
neighAveOutSE Z * novel [Held Out]	0.970 (0.917 – 1.025)	0.028	-1.085	0.278
neighAveOutSE Z * outDegree Z	1.089 (1.048 – 1.132)	0.021	4.375	<0.001
novel [Held Out] * outDegree Z	1.024 (0.970 – 1.081)	0.028	0.860	0.390
(neighAveOutSE Z * novel [Held Out]) * outDegree Z	0.958 (0.904 – 1.016)	0.029	-1.419	0.156
Random Effects
σ2	3.29
τ00 subID	0.63
τ00 trait	0.12
τ11 subID.neighAveOutSE.Z	0.24
τ11 subID.novelHeld Out	0.00
ρ01 subID.neighAveOutSE.Z	-0.07
ρ01 subID.novelHeld Out	-1.00
N subID	200
N trait	147
Observations	29060
Marginal R2 / Conditional R2	0.033 / NA

Inwards Neighboring Self-Evaluations

m <- glmer( ingChoiceN ~ neighAveInSE.Z * novel * outDegree.Z + desirability.Z + ( neighAveInSE.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)

## boundary (singular) fit: see help('isSingular')

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.710 (1.494 – 1.957)	0.118	7.806	<0.001
neighAveInSE Z	1.186 (1.091 – 1.289)	0.051	4.001	<0.001
novel [Held Out]	0.986 (0.935 – 1.040)	0.027	-0.517	0.605
outDegree Z	1.063 (0.987 – 1.144)	0.040	1.607	0.108
desirability Z	1.223 (1.140 – 1.313)	0.044	5.600	<0.001
neighAveInSE Z * novel [Held Out]	0.974 (0.922 – 1.028)	0.027	-0.963	0.336
neighAveInSE Z * outDegree Z	1.078 (1.039 – 1.118)	0.020	4.032	<0.001
novel [Held Out] * outDegree Z	1.021 (0.968 – 1.077)	0.028	0.761	0.447
(neighAveInSE Z * novel [Held Out]) * outDegree Z	0.957 (0.904 – 1.013)	0.028	-1.527	0.127
Random Effects
σ2	3.29
τ00 subID	0.67
τ00 trait	0.13
τ11 subID.neighAveInSE.Z	0.20
τ11 subID.novelHeld Out	0.00
ρ01 subID.neighAveInSE.Z	0.06
ρ01 subID.novelHeld Out	-0.60
N subID	200
N trait	148
Observations	29282
Marginal R2 / Conditional R2	0.028 / NA

Does generalization depend on indegree?

Similarity-Weighted Self-Evaluations

m <- glmer( ingChoiceN ~ WSR.Z * novel * inDegree.Z + desirability.Z + ( WSR.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)

## boundary (singular) fit: see help('isSingular')

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.663 (1.370 – 2.020)	0.165	5.138	<0.001
WSR Z	1.890 (1.448 – 2.466)	0.257	4.682	<0.001
novel [Held Out]	0.984 (0.933 – 1.038)	0.027	-0.606	0.545
inDegree Z	0.982 (0.919 – 1.049)	0.033	-0.535	0.592
desirability Z	1.249 (1.173 – 1.329)	0.040	6.938	<0.001
WSR Z * novel [Held Out]	0.963 (0.912 – 1.017)	0.027	-1.341	0.180
WSR Z * inDegree Z	1.050 (1.014 – 1.086)	0.018	2.775	0.006
novel [Held Out] * inDegree Z	1.048 (0.995 – 1.105)	0.028	1.760	0.078
(WSR Z * novel [Held Out]) * inDegree Z	0.957 (0.907 – 1.010)	0.026	-1.581	0.114
Random Effects
σ2	3.29
τ00 subID	1.26
τ00 trait	0.12
τ11 subID.WSR.Z	2.59
τ11 subID.novelHeld Out	0.00
ρ01 subID.WSR.Z	0.06
ρ01 subID.novelHeld Out	0.79
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.120 / NA

ggpredict(m, c("WSR.Z", "novel")) %>% plot(show.title=F) + xlab("Similarity-Weighted Self-Descriptiveness") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()

## Data were 'prettified'. Consider using `terms="WSR.Z [all]"` to get smooth plots.

Cross-Validated Expectations

m <- glmer( ingChoiceN ~ predicted.Z * novel * inDegree.Z + desirability.Z + ( predicted.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)

## boundary (singular) fit: see help('isSingular')

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.641 (1.425 – 1.891)	0.119	6.858	<0.001
predicted Z	1.577 (1.311 – 1.896)	0.148	4.839	<0.001
novel [Held Out]	0.990 (0.938 – 1.044)	0.027	-0.387	0.699
inDegree Z	0.981 (0.916 – 1.051)	0.034	-0.544	0.587
desirability Z	1.261 (1.181 – 1.346)	0.042	6.954	<0.001
predicted Z * novel [Held Out]	0.961 (0.910 – 1.014)	0.026	-1.460	0.144
predicted Z * inDegree Z	1.045 (1.011 – 1.081)	0.018	2.567	0.010
novel [Held Out] * inDegree Z	1.042 (0.989 – 1.098)	0.028	1.552	0.121
(predicted Z * novel [Held Out]) * inDegree Z	0.960 (0.910 – 1.012)	0.026	-1.506	0.132
Random Effects
σ2	3.29
τ00 subID	0.53
τ00 trait	0.13
τ11 subID.predicted.Z	0.64
τ11 subID.novelHeld Out	0.00
ρ01 subID.predicted.Z	0.26
ρ01 subID.novelHeld Out	-0.40
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.072 / NA

Dot-Product Expected Ratings

m <- glmer( ingChoiceN ~ er.Z * novel * inDegree.Z + desirability.Z + ( er.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)

## boundary (singular) fit: see help('isSingular')

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.590 (1.330 – 1.901)	0.145	5.085	<0.001
er Z	1.418 (1.124 – 1.788)	0.168	2.949	0.003
novel [Held Out]	0.980 (0.929 – 1.033)	0.027	-0.761	0.447
inDegree Z	0.988 (0.923 – 1.058)	0.034	-0.338	0.735
desirability Z	1.263 (1.184 – 1.347)	0.042	7.071	<0.001
er Z * novel [Held Out]	0.961 (0.910 – 1.014)	0.026	-1.469	0.142
er Z * inDegree Z	1.049 (1.014 – 1.086)	0.018	2.763	0.006
novel [Held Out] * inDegree Z	1.046 (0.992 – 1.102)	0.028	1.673	0.094
(er Z * novel [Held Out]) * inDegree Z	0.961 (0.910 – 1.014)	0.026	-1.463	0.144
Random Effects
σ2	3.29
τ00 subID	1.02
τ00 trait	0.13
τ11 subID.er.Z	1.95
τ11 subID.novelHeld Out	0.00
ρ01 subID.er.Z	-0.01
ρ01 subID.novelHeld Out	-0.40
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.050 / NA

Linear Trend of Self-Descriptiveness

m <- glmer( ingChoiceN ~ slope.Z * novel * inDegree.Z + desirability.Z + ( slope.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)

## boundary (singular) fit: see help('isSingular')

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.655 (1.382 – 1.983)	0.152	5.479	<0.001
slope Z	1.535 (1.201 – 1.962)	0.192	3.426	0.001
novel [Held Out]	0.981 (0.930 – 1.034)	0.027	-0.713	0.476
inDegree Z	0.988 (0.924 – 1.058)	0.034	-0.335	0.738
desirability Z	1.260 (1.182 – 1.345)	0.042	7.024	<0.001
slope Z * novel [Held Out]	0.959 (0.908 – 1.012)	0.026	-1.534	0.125
slope Z * inDegree Z	1.050 (1.015 – 1.087)	0.018	2.817	0.005
novel [Held Out] * inDegree Z	1.045 (0.991 – 1.101)	0.028	1.623	0.104
(slope Z * novel [Held Out]) * inDegree Z	0.961 (0.910 – 1.014)	0.026	-1.464	0.143
Random Effects
σ2	3.29
τ00 subID	1.01
τ00 trait	0.13
τ11 subID.slope.Z	2.13
τ11 subID.novelHeld Out	0.00
ρ01 subID.slope.Z	-0.05
ρ01 subID.novelHeld Out	-0.50
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.066 / NA

All Neighboring Self-Evaluations

m <- glmer( ingChoiceN ~ neighAveAllSE.Z * novel * inDegree.Z + desirability.Z + ( neighAveAllSE.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)

## boundary (singular) fit: see help('isSingular')

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.701 (1.488 – 1.945)	0.116	7.777	<0.001
neighAveAllSE Z	1.262 (1.137 – 1.402)	0.068	4.355	<0.001
novel [Held Out]	0.989 (0.938 – 1.044)	0.027	-0.392	0.695
inDegree Z	0.990 (0.925 – 1.059)	0.034	-0.300	0.764
desirability Z	1.262 (1.184 – 1.345)	0.041	7.120	<0.001
neighAveAllSE Z * novel [Held Out]	0.976 (0.924 – 1.030)	0.027	-0.886	0.376
neighAveAllSE Z * inDegree Z	1.063 (1.026 – 1.101)	0.019	3.379	0.001
novel [Held Out] * inDegree Z	1.043 (0.990 – 1.099)	0.028	1.577	0.115
(neighAveAllSE Z * novel [Held Out]) * inDegree Z	0.949 (0.898 – 1.003)	0.027	-1.857	0.063
Random Effects
σ2	3.29
τ00 subID	0.63
τ00 trait	0.13
τ11 subID.neighAveAllSE.Z	0.36
τ11 subID.novelHeld Out	0.00
ρ01 subID.neighAveAllSE.Z	-0.07
ρ01 subID.novelHeld Out	-0.52
N subID	200
N trait	148
Observations	29329
Marginal R2 / Conditional R2	0.033 / NA

Outwards Neighboring Self-Evaluations

m <- glmer( ingChoiceN ~ neighAveOutSE.Z * novel * inDegree.Z + desirability.Z + ( neighAveOutSE.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)

## boundary (singular) fit: see help('isSingular')

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.722 (1.508 – 1.966)	0.116	8.035	<0.001
neighAveOutSE Z	1.215 (1.114 – 1.326)	0.054	4.374	<0.001
novel [Held Out]	0.983 (0.932 – 1.037)	0.027	-0.628	0.530
inDegree Z	0.986 (0.922 – 1.055)	0.034	-0.402	0.688
desirability Z	1.263 (1.185 – 1.346)	0.041	7.174	<0.001
neighAveOutSE Z * novel [Held Out]	0.973 (0.922 – 1.028)	0.027	-0.973	0.330
neighAveOutSE Z * inDegree Z	1.053 (1.017 – 1.091)	0.019	2.887	0.004
novel [Held Out] * inDegree Z	1.049 (0.995 – 1.105)	0.028	1.760	0.078
(neighAveOutSE Z * novel [Held Out]) * inDegree Z	0.946 (0.896 – 1.000)	0.026	-1.971	0.049
Random Effects
σ2	3.29
τ00 subID	0.64
τ00 trait	0.13
τ11 subID.neighAveOutSE.Z	0.24
τ11 subID.novelHeld Out	0.00
ρ01 subID.neighAveOutSE.Z	-0.07
ρ01 subID.novelHeld Out	-1.00
N subID	200
N trait	147
Observations	29060
Marginal R2 / Conditional R2	0.029 / NA

Inwards Neighboring Self-Evaluations

m <- glmer( ingChoiceN ~ neighAveInSE.Z * novel * inDegree.Z + desirability.Z + ( neighAveInSE.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)

## boundary (singular) fit: see help('isSingular')

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.710 (1.494 – 1.957)	0.118	7.800	<0.001
neighAveInSE Z	1.200 (1.103 – 1.306)	0.052	4.222	<0.001
novel [Held Out]	0.985 (0.934 – 1.039)	0.027	-0.563	0.574
inDegree Z	0.994 (0.928 – 1.064)	0.035	-0.187	0.852
desirability Z	1.259 (1.181 – 1.344)	0.042	6.985	<0.001
neighAveInSE Z * novel [Held Out]	0.972 (0.920 – 1.027)	0.027	-1.011	0.312
neighAveInSE Z * inDegree Z	1.063 (1.025 – 1.102)	0.019	3.324	0.001
novel [Held Out] * inDegree Z	1.046 (0.992 – 1.102)	0.028	1.673	0.094
(neighAveInSE Z * novel [Held Out]) * inDegree Z	0.968 (0.916 – 1.023)	0.027	-1.153	0.249
Random Effects
σ2	3.29
τ00 subID	0.66
τ00 trait	0.13
τ11 subID.neighAveInSE.Z	0.20
τ11 subID.novelHeld Out	0.00
ρ01 subID.neighAveInSE.Z	0.05
ρ01 subID.novelHeld Out	-0.56
N subID	200
N trait	148
Observations	29282
Marginal R2 / Conditional R2	0.027 / NA

Backwards solution: Can you predict self-evaluations from similarity to ingroup and outgroup choices?

Main effect

m <- lmer( scale(selfResp) ~ scale(inGsim) + scale(outGsim) + (  scale(inGsim) + scale(outGsim) | subID) + (1 | trait), data = fullTrain)

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0027381 (tol = 0.002, component 1)

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "B", string.se = "SE", string.stat = "t", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	B	SE	t	p
(Intercept)	-0.020 (-0.096 – 0.057)	0.039	-0.505	0.614
inGsim	0.163 (0.113 – 0.214)	0.026	6.352	<0.001
outGsim	-0.024 (-0.069 – 0.021)	0.023	-1.043	0.297
Random Effects
σ2	0.68
τ00 subID	0.17
τ00 trait	0.09
τ11 subID.scale(inGsim)	0.05
τ11 subID.scale(outGsim)	0.05
ρ01 subID.scale(inGsim)	0.05
ρ01 subID.scale(outGsim)	-0.03
ICC	0.31
N subID	197
N trait	148
Observations	17362
Marginal R2 / Conditional R2	0.022 / 0.323

Moderated by condition

m <- lmer( scale(selfResp) ~ scale(inGsim)*outgroup + scale(outGsim)*outgroup + (  scale(inGsim) + scale(outGsim) | subID) + ( outgroup | trait), data = fullTrain)

## boundary (singular) fit: see help('isSingular')

## Warning: Model failed to converge with 1 negative eigenvalue: -2.9e+01

tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "B", string.se = "SE", string.stat = "t", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	B	SE	t	p
(Intercept)	0.001 (-0.113 – 0.115)	0.058	0.021	0.983
inGsim	0.174 (0.095 – 0.252)	0.040	4.312	<0.001
outgroup [Not UCR]	0.024 (-0.122 – 0.170)	0.074	0.317	0.751
outgroup [UCLA]	-0.086 (-0.231 – 0.060)	0.074	-1.155	0.248
outGsim	-0.007 (-0.080 – 0.066)	0.037	-0.186	0.852
inGsim * outgroup [Not UCR]	0.031 (-0.070 – 0.131)	0.051	0.602	0.547
inGsim * outgroup [UCLA]	-0.042 (-0.139 – 0.055)	0.049	-0.854	0.393
outgroup [Not UCR] * outGsim	-0.076 (-0.176 – 0.024)	0.051	-1.485	0.138
outgroup [UCLA] * outGsim	0.021 (-0.076 – 0.118)	0.050	0.420	0.675
Random Effects
σ2	0.68
τ00 subID	0.17
τ00 trait	0.09
τ11 subID.scale(inGsim)	0.05
τ11 subID.scale(outGsim)	0.05
τ11 trait.outgroupNot UCR	0.00
τ11 trait.outgroupUCLA	0.00
ρ01	0.03
	-0.01
	-1.00
	-0.77
N subID	197
N trait	148
Observations	17362
Marginal R2 / Conditional R2	0.036 / NA

ggpredict(m, c("inGsim","outgroup")) %>% plot(show.title=F) + xlab("Similarity to Ingroup Choices") + ylab("Self-Evaluation") + jtools::theme_apa()

Moderated by Group Homophily

People who mentally segregrate the groups also self-evaluate more similar to their later group choices.

# m <- lmer( scale(selfResp) ~ scale(inGsim) * scale(groupHomoph) + scale(outGsim) + (  scale(inGsim) + scale(outGsim) | subID) + (1 | trait), data = fullTrain)
# summary(m)
# tidy(m,conf.int=TRUE,effects="fixed")
# ggpredict(m, c("inGsim","groupHomoph")) %>% plot(show.title=F) + xlab("Similarity to Ingroup Choices") + ylab("Self-Evaluation") + jtools::theme_apa()

Individual differences moderation of self-anchoring

More self-projection for those who perceive group as more entitative?

m <- glmer( ingChoiceN ~ WSR.Z * Ent.Z + ( WSR.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.627 (1.335 – 1.983)	0.164	4.824	<0.001
WSR Z	1.857 (1.418 – 2.432)	0.255	4.501	<0.001
Ent Z	1.133 (0.934 – 1.373)	0.111	1.269	0.204
WSR Z * Ent Z	1.117 (0.862 – 1.448)	0.148	0.839	0.402
Random Effects
σ2	3.29
τ00 subID	1.28
τ00 trait	0.17
τ11 subID.WSR.Z	2.60
ρ01 subID	0.07
ICC	0.55
N subID	199
N trait	148
Observations	29184
Marginal R2 / Conditional R2	0.058 / 0.577

More self-projection for those who have been at UCR for shorter time?

m <- glmer( ingChoiceN ~ WSR.Z * Years + ( WSR.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	2.180 (1.315 – 3.614)	0.562	3.023	0.003
WSR Z	1.726 (0.891 – 3.344)	0.582	1.618	0.106
Years	0.881 (0.717 – 1.082)	0.092	-1.212	0.225
WSR Z * Years	1.037 (0.797 – 1.349)	0.139	0.271	0.786
Random Effects
σ2	3.29
τ00 subID	1.28
τ00 trait	0.17
τ11 subID.WSR.Z	2.58
ρ01 subID	0.07
ICC	0.55
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.051 / 0.573

More self-projection for higher self-esteem?

Higher self-esteem people self-project more

m <- glmer( ingChoiceN ~ WSR.Z * SE + ( WSR.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	0.787 (0.556 – 1.114)	0.140	-1.349	0.177
WSR Z	1.404 (1.054 – 1.871)	0.206	2.317	0.020
SE	19.366 (6.316 – 59.377)	11.070	5.184	<0.001
WSR Z * SE	2.529 (1.602 – 3.993)	0.589	3.983	<0.001
Random Effects
σ2	3.29
τ00 subID	1.26
τ00 trait	0.15
τ11 subID.WSR.Z	2.57
ρ01 subID	0.07
ICC	0.55
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.049 / 0.570

ggpredict(m, c("SE","WSR.Z")) %>% plot(show.title=F) + xlab("Self-Esteem") + ylab("Self-Evaluation") + jtools::theme_apa()

## Data were 'prettified'. Consider using `terms="SE [all]"` to get smooth plots.

Do people higher in social identification self-project more?

No evidence

m <- glmer( ingChoiceN ~ WSR.Z * MGIS + ( WSR.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	0.676 (0.284 – 1.611)	0.299	-0.883	0.377
WSR Z	1.177 (0.393 – 3.527)	0.659	0.291	0.771
MGIS	1.208 (1.006 – 1.451)	0.113	2.026	0.043
WSR Z * MGIS	1.097 (0.869 – 1.384)	0.130	0.776	0.438
Random Effects
σ2	3.29
τ00 subID	1.20
τ00 trait	0.17
τ11 subID.WSR.Z	2.52
ρ01 subID	0.05
ICC	0.54
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.064 / 0.571

Do people higher in need for cognition self-project more?

m <- glmer( ingChoiceN ~ WSR.Z * NFC + ( WSR.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	3.682 (1.335 – 10.157)	1.906	2.518	0.012
WSR Z	1.527 (0.395 – 5.910)	1.054	0.613	0.540
NFC	0.809 (0.624 – 1.048)	0.107	-1.604	0.109
WSR Z * NFC	1.060 (0.752 – 1.493)	0.185	0.331	0.741
Random Effects
σ2	3.29
τ00 subID	1.28
τ00 trait	0.17
τ11 subID.WSR.Z	2.61
ρ01 subID	0.07
ICC	0.55
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.048 / 0.573

Do people higher in need to belong project more?

m <- glmer( ingChoiceN ~ WSR.Z * NTB + ( WSR.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	1.923 (0.698 – 5.303)	0.995	1.264	0.206
WSR Z	2.010 (0.519 – 7.787)	1.389	1.010	0.312
NTB	0.952 (0.704 – 1.287)	0.147	-0.320	0.749
WSR Z * NTB	0.978 (0.651 – 1.468)	0.203	-0.109	0.914
Random Effects
σ2	3.29
τ00 subID	1.29
τ00 trait	0.17
τ11 subID.WSR.Z	2.59
ρ01 subID	0.06
ICC	0.55
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.051 / 0.574

Do people higher in self-prototypicality self-project more?

m <- glmer( ingChoiceN ~ WSR.Z * Proto + ( WSR.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
tab_model(m, collapse.ci = T, show.stat=T, show.r2 = T, show.se = T, string.pred = c("Fixed Effects"), string.est = "OR", string.se = "SE", string.stat = "z", digits = 3, emph.p = F, dv.labels = "Ingroup Prediction")

	Ingroup Prediction
Fixed Effects	OR	SE	z	p
(Intercept)	0.851 (0.434 – 1.670)	0.293	-0.469	0.639
WSR Z	1.094 (0.445 – 2.691)	0.502	0.195	0.845
Proto	1.127 (0.999 – 1.272)	0.069	1.949	0.051
WSR Z * Proto	1.099 (0.935 – 1.291)	0.091	1.141	0.254
Random Effects
σ2	3.29
τ00 subID	1.21
τ00 trait	0.17
τ11 subID.WSR.Z	2.54
ρ01 subID	0.04
ICC	0.54
N subID	200
N trait	148
Observations	29331
Marginal R2 / Conditional R2	0.062 / 0.572